The hippocampus creates distinct episodes from highly similar events through a process called pattern separation and can retrieve memories from partial or degraded cues through a process called pattern completion. These processes have been studied in humans using tasks where participants must distinguish studied items from perceptually similar lure items. False alarms to lures (incorrectly reporting a perceptually similar item as previously studied) are thought to reflect pattern completion, a retrieval-based process. However, false alarms to lures could also result from insufficient encoding of studied items, leading to impoverished memory of item details and a failure to correctly reject lures. The current study investigated the source of lure false alarms by comparing eye movements during the initial presentation of items to eye movements made during the later presentation of item repetitions and similar lures in order to assess mnemonic processing at encoding and retrieval, respectively. Relative to other response types, lure false alarms were associated with fewer fixations to the initially studied items, suggesting that false alarms result from impoverished encoding. Additionally, lure correct rejections and lure false alarms garnered more fixations than hits, denoting additional retrieval-related processing. The results suggest that measures of pattern separation and completion in behavioral paradigms are not process-pure.
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http://dx.doi.org/10.1002/hipo.22256 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Key Laboratory of Ocean Observation and Forecasting, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266000, China.
Tropical cyclones (TCs), particularly those that rapidly intensify (RI), pose a significant threat due to the uncertainty in forecasting them. RI TC periods, which intensify by at least 13 m/s within 24 h, remain challenging to forecast accurately. Existing models achieve a probability of detection (POD) of 82.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Department of Information Engineering (DEI), University of Padova, Via G. Gradenigo 6/B, Padua, 35131, Italy.
Background: Post bariatric hypoglycaemic (PBH) is a late complication of weight loss surgery, characterised by critically low blood glucose levels following meal-induced glycaemic excursions. The disabling consequences of PBH underline the need for the development of a decision support system (DSS) that can warn individuals about upcoming PBH events, thus enabling preventive actions to avoid impending episodes. In view of this, we developed various algorithms based on linear and deep learning models to forecast PBH episodes in the short-term.
View Article and Find Full Text PDFBrain Behav
January 2025
Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada.
Introduction: Patients with bipolar disorder (BD) demonstrate episodic memory deficits, which may be hippocampal-dependent and may be attenuated in lithium responders. Induced pluripotent stem cell-derived CA3 pyramidal cell-like neurons show significant hyperexcitability in lithium-responsive BD patients, while lithium nonresponders show marked variance in hyperexcitability. We hypothesize that this variable excitability will impair episodic memory recall, as assessed by cued retrieval (pattern completion) within a computational model of the hippocampal CA3.
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December 2025
Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001 China.
Seizure prediction based on electroencephalogram (EEG) for people with epilepsy, a common brain disorder worldwide, has great potential for life quality improvement. To alleviate the high degree of heterogeneity among patients, several works have attempted to learn common seizure feature distributions based on the idea of domain adaptation to enhance the generalization ability of the model. However, existing methods ignore the inherent inter-patient discrepancy within the source patients, resulting in disjointed distributions that impede effective domain alignment.
View Article and Find Full Text PDFWater Res X
December 2024
Professor, Department of Civil and Architectural Engineering and Mechanics, The University of Arizona, Tucson, AZ 85721, USA.
Smart meters such as advanced metering infrastructure (AMI) can significantly improve identifying realistic sized leaks in water distribution networks (WDNs). However, to date, detection/localization methods for AMI systems are extremely limited. In this study, to examine the benefits of using AMIs for leak detection within distribution network, a three-dimensional (3D) convolutional neural network (CNN) deep learning (DL) model is proposed that can account for temporally and spatially distributed information of pressures.
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